Papers by Yijun Mo
Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition (2022.findings-acl)
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| Challenge: | Existing studies use one attention mechanism to improve contextual semantic representation learning for implicit discourse relation recognition (IDRR). |
| Approach: | They propose a Multi-Attentive Neural Fusion model to fuse linguistic evidence and semantic connection for IDRR by using a Dual Attention Network and an Offset Matrix Network. |
| Outcome: | The proposed model achieves state-of-the-art on the PDTB 3.0 corpus. |
NCPrompt: NSP-Based Prompt Learning and Contrastive Learning for Implicit Discourse Relation Recognition (2024.findings-emnlp)
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| Challenge: | Recent prompt learning methods have demonstrated success in IDRR, but they fail to fully exploit critical semantic features shared among various forms of templates. |
| Approach: | They propose an NSP-based prompt learning and contrastive learning method for IDRR that transforms the IDRR task into a next sentence prediction task. |
| Outcome: | The proposed model can be used to classify the discourse relation sense between argument pairs without an explicit connective. |
Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction (2022.emnlp-main)
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| Challenge: | Existing prompt-tuning methods for event argument extraction lack entity information . eAE is a key step of event extraction, but it requires a pre-trained language model to extract event arguments. |
| Approach: | They propose a prompt-tuning method that takes advantage of entity information and pre-trained language models. |
| Outcome: | The proposed method outperforms the state-of-the-art prompt-tuning methods on an english dataset. |